Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control

Sha'aban, Yusuf A, Ikpehai, Augustine ORCID: https://orcid.org/0000-0002-5254-8188, Adebisi, Bamidele ORCID: https://orcid.org/0000-0001-9071-9120 and Rabie, Khaled M ORCID: https://orcid.org/0000-0002-9784-3703 (2017) Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control. Energies, 10 (10). 1507. ISSN 1996-1073

Preview

Published Version Available under License Creative Commons Attribution. Download (410kB) | Preview

Abstract

The emergence of plug-in electric vehicles (PEVs) is unveiling new opportunities to de-carbonise the vehicle parcs and promote sustainability in different parts of the globe. As battery technologies and PEV efficiency continue to improve, the use of electric cars as distributed energy resources is fast becoming a reality. While the distribution network operators (DNOs) strive to ensure grid balancing and reliability, the PEV owners primarily aim at maximising their economic benefits. However, given that the PEV batteries have limited capacities and the distribution network is constrained, smart techniques are required to coordinate the charging/discharging of the PEVs. Using the economic model predictive control (EMPC) technique, this paper proposes a decentralised optimisation algorithm for PEVs during the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations. To capture the operational dynamics of the batteries, it considers the state-of-charge (SoC) at a given time as a discrete state space and investigates PEVs performance in V2G and G2V operations. In particular, this study exploits the variability in the energy tariff across different periods of the day to schedule V2G/G2V cycles using real data from the university’s PEV infrastructure. The results show that by charging/discharging the vehicles during optimal time partitions, prosumers can take advantage of the price elasticity of supply to achieve net savings of about 63%.